Volodymyr Kuleshov's repositories
audio-super-res
Audio super resolution using neural networks
teaching-material
Teaching materials for the machine learning and deep learning classes at Stanford and Cornell
cornell-cs5785-2020-applied-ml
Teaching materials for the applied machine learning course at Cornell Tech (online edition)
cornell-cs5785-2023-applied-ml
Lecture materials for Cornell CS5785 Applied Machine Learning (Fall 2023)
tensor-factorization
Tensor Factorization via Matrix Factorization
cs228-notes
Lecture notes on probabilistic graphical modeling, based on Stanford CS228 (work in progress!)
online-learning
A few basic online learning algorithms
deep-learning-models
Implementations of popular deep learning models in Theano+Lasagne
deep-hybrid-models
Deep hybrid models: bridging discriminative and generative approaches https://cs.stanford.edu/~ermon/papers/uai2017_cr.pdf
neural-variational-inference
Neural variational inference and learning in undirected graphical models http://www.stanford.edu/~kuleshov/papers/nips2017.pdf
generalized-rayleigh-quotient
Fast algorithms for sparse principal component analysis
convolutional-draw
Tensorflow implementation of Convolutional DRAW by Gregor et al. (2016)
diffusion-llm
Simplified Masked Diffusion Language Model
Glow-PyTorch
Simple, extendable, easy to understand Glow implementation in PyTorch
improved-gan
code for the paper "Improved Techniques for Training GANs"
NeuralRandomFieldLearning
http://web.stanford.edu/~kuleshov/papers/iclr2016.pdf